U.S. patent number 11,361,648 [Application Number 17/254,968] was granted by the patent office on 2022-06-14 for fall detection apparatus, a method of detecting a fall by a subject and a computer program product for implementing the method.
This patent grant is currently assigned to Koninklijke Philips N.V.. The grantee listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Warner Rudolph Theophile Ten Kate, Doortje Van De Wouw.
United States Patent |
11,361,648 |
Ten Kate , et al. |
June 14, 2022 |
Fall detection apparatus, a method of detecting a fall by a subject
and a computer program product for implementing the method
Abstract
According to an aspect, there is provided a fall detection
apparatus, the fall detection apparatus comprising one or more
processing units configured to obtain a first input indicating
which one or ones of a plurality of fall detection algorithms have
detected a potential fall by the subject, wherein each fall
detection algorithm of the plurality of fall detection algorithms
is associated with a respective type of fall and detects a
potential fall of the associated type by analysing a set of
movement measurements for the subject, wherein each respective type
of fall has an associated initial state of the subject; obtain a
second input indicating the status of the subject prior to the
potential fall, wherein the status of the subject is determined by
analysing a set of measurements from one or more sensors in the
environment of the subject; compare the determined status of the
subject prior to the potential fall to the initial state for each
type of fall associated with any potential fall indicated in the
first input; and output an indication that the subject has fallen
if the determined status of the subject matches the initial state
of any of the respective types of fall associated with any
potential fall indicated in the first input.
Inventors: |
Ten Kate; Warner Rudolph
Theophile (Waalre, NL), Van De Wouw; Doortje
(Eindhoven, NL) |
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
Eindhoven |
N/A |
NL |
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Assignee: |
Koninklijke Philips N.V.
(Eindhoven, NL)
|
Family
ID: |
1000006369190 |
Appl.
No.: |
17/254,968 |
Filed: |
June 24, 2019 |
PCT
Filed: |
June 24, 2019 |
PCT No.: |
PCT/EP2019/066571 |
371(c)(1),(2),(4) Date: |
December 22, 2020 |
PCT
Pub. No.: |
WO2020/002175 |
PCT
Pub. Date: |
January 02, 2020 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20210150872 A1 |
May 20, 2021 |
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Foreign Application Priority Data
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Jun 29, 2018 [EP] |
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18180769 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G08B
21/043 (20130101) |
Current International
Class: |
G08B
21/04 (20060101) |
Field of
Search: |
;340/573.7,539.1 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2009230335 |
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Oct 2009 |
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JP |
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2016092487 |
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Jun 2016 |
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WO |
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2018029193 |
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Feb 2018 |
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WO |
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2018069262 |
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Apr 2018 |
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WO |
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Other References
International Search Report and Written Opinion, International
Application No. PCT/EP2019/066571, dated Sep. 13, 2019. cited by
applicant .
Delahoz, Y. et al., "Survey on Fall Detection and Fall Prevention
Using Wearable and External Sensors", Sensors 2014, 14,
19806-19842. cited by applicant.
|
Primary Examiner: King; Curtis J
Claims
The invention claimed is:
1. A fall detection apparatus, the fall detection apparatus
comprising one or more processing units configured to: obtain a
first input indicating which one or ones of a plurality of fall
detection algorithms have detected a potential fall by the subject,
wherein each fall detection algorithm of the plurality of fall
detection algorithms is associated with a respective type of fall
and detects a potential fall of the associated type by analyzing a
set of movement measurements for the subject, wherein each
respective type of fall has an associated initial state of the
subject; obtain a second input indicating the status of the subject
prior to the potential fall, wherein the status of the subject is
determined by analyzing a set of measurements from one or more
sensors in the environment of the subject; compare the determined
status of the subject prior to the potential fall to the initial
state for each type of fall associated with any potential fall
indicated in the first input; filter or validate the first input
based on the determined status of the subject; output an indication
that the subject has fallen if the determined status of the subject
matches the initial state of any of the respective types of fall
associated with any potential fall indicated in the first input;
and dismiss or ignore a first potential fall indicated in the first
input if the determined status of the subject does not match the
initial state of the respective type of fall associated with the
first potential fall indicated in the first input.
2. The fall detection apparatus as claimed in claim 1, wherein the
initial state of the subject associated with a type of fall
comprises any one or more of: (i) a standing posture, (ii) a seated
posture, and (iii) a lying posture.
3. The fall detection apparatus as claimed in claim 1, wherein the
respective types of falls associated with the plurality of fall
detection algorithms comprise any one or more of: (i) a fall from a
standing posture, (ii) a fall from a seated posture, (iii) a fall
from a lying posture, (iv) a fall when moving from a seated posture
to a standing posture, (v) a fall when moving from a standing
posture to a sitting posture, (vi) a fall from a standing posture
onto furniture, (vii) a fall from a standing posture in which the
subject slides down a wall.
4. The fall detection apparatus as claimed in claim 1, wherein the
one or more processing units are configured to obtain the first
input by: analyzing a set of movement measurements for a subject
using the plurality of fall detection algorithms to detect whether
there has been a potential fall by the subject of the respective
type associated with each fall detection algorithm; and forming the
first input from the result of the analysis of the set of movement
measurements using the plurality of fall detection algorithms.
5. The fall detection apparatus as claimed in claim 1, wherein the
one or more processing units are configured to obtain the first
input from a fall detection device that is carried or worn by the
subject.
6. The fall detection apparatus as claimed in claim 1, wherein the
one or more processing units are configured to obtain the second
input by: analyzing a set of measurements from one or more sensors
in the environment of the subject to determine the status of the
subject prior to a potential fall; and forming the second input
from the result of the analysis of the set of measurements from one
or more sensors in the environment of the subject.
7. The fall detection apparatus as claimed in claim 1, wherein the
one or more processing units are configured to obtain the second
input from a monitoring system that includes the one or more
sensors in the environment of the subject.
8. The fall detection apparatus as claimed in claim 1, wherein the
set of movement measurements comprises at least one measurement
from an air pressure sensor.
9. A fall detection device, comprising: a personal help button; one
or more movement sensors for measuring the movements of a subject;
one or more processing units configured to: receive a set of
movement measurements for the subject from the one or more movement
sensors; analyze the set of movement measurements using a plurality
of fall detection algorithms to detect whether there has been a
potential fall by the subject of a respective type of fall
associated with each fall detection algorithm, wherein each
respective type of fall has an associated initial state of the
subject; and form a first input from the result of the analysis of
the set of movement measurements using the plurality of fall
detection algorithms; and the fall detection apparatus as claimed
in claim 1.
10. A monitoring system, comprising: the fall detection apparatus
as claimed in claim 1, and one or more processing units configured
to: receive the set of measurements from one or more sensors in the
environment of the subject; analyze the set of measurements to
determine the status of the subject prior to a potential fall; and
form the second input from the result of the analysis of the set of
measurements.
11. A method of detecting a fall, the method comprising: obtaining
a first input indicating which one or ones of a plurality of fall
detection algorithms have detected a potential fall by the subject,
wherein each fall detection algorithm of the plurality of fall
detection algorithms is associated with a respective type of fall
and detects a potential fall of the associated type by analyzing a
set of movement measurements for the subject, wherein each
respective type of fall has an associated initial state of the
subject; obtaining a second input indicating the status of the
subject prior to the potential fall, wherein the status of the
subject is determined by analyzing a set of measurements from one
or more sensors in the environment of the subject; comparing the
determined status of the subject prior to the potential fall to the
initial state for each type of fall associated with any potential
fall indicated in the first input; filtering or validating the
first input based on the determined status of the subject; and
outputting an indication that the subject has fallen if the
determined status of the subject matches the initial state of any
of the respective types of fall associated with any potential fall
indicated in the first input.
12. The method as claimed in claim 11, wherein the step of
obtaining the first input comprises: analyzing a set of movement
measurements for a subject using the plurality of fall detection
algorithms to detect whether there has been a potential fall by the
subject of the respective type associated with each fall detection
algorithm; and forming the first input from the result of the
analysis of the set of movement measurements using the plurality of
fall detection algorithms.
13. The method as claimed in claim 11, wherein the step of
obtaining the first input comprises obtaining the first input from
a fall detection device that is carried or worn by the subject.
14. The method as claimed in claim 11, wherein the step of
obtaining the second input comprises: analyzing a set of
measurements from one or more sensors in the environment of the
subject to determine the status of the subject prior to a potential
fall; and forming the second input from the result of the analysis
of the set of measurements from one or more sensors in the
environment of the subject.
15. The method as claimed in claim 11, wherein the step of
obtaining the second input comprises obtaining the second input
from a monitoring system that includes the one or more sensors in
the environment of the subject.
16. The method as claimed in claim 11, wherein the set of movement
measurements comprise at least one measurement from an air pressure
sensor.
17. A computer program product comprising a non-transitory computer
readable medium having computer readable code embodied therein, the
computer readable code being configured such that, on execution by
a suitable computer or processor, the computer or processor is
caused to perform the method of claim 11.
Description
CROSS-REFERENCE TO PRIOR APPLICATIONS
This application is the U.S. National Phase application under 35
U.S.C. .sctn. 371 of International Application No.
PCT/EP2019/066571, filed on 24 Jun. 2019, which claims the benefit
of European Patent Application No. 18180769.4, filed on 29 Jun.
2018. These applications are hereby incorporated by reference
herein.
FIELD OF THE INVENTION
The disclosure relates to the detection of falls by a subject, and
in particular to a fall detection apparatus, a method of detecting
a fall by a subject and a computer program product for implementing
the method that can detect a number of different types of fall.
BACKGROUND OF THE INVENTION
With ageing, physical ability declines. A person's mobility may be
affected and they may experience difficulty in maintaining their
independence. A large category of difficulties concern falls, which
may have dramatic outcomes to the health state of the person
falling.
Falls affect millions of people each year and result in significant
injuries, particularly among the elderly. In fact, it has been
estimated that falls are one of the top three causes of death in
elderly people. A fall is defined as a sudden, uncontrolled and
unintentional downward displacement of the body to the ground,
followed by an impact, after which the body stays down on the
ground.
A personal emergency response system (PERS) is a system in which
help for a subject can be requested. By means of Personal Help
Buttons (PHBs) the subject can push the button to summon help in an
emergency. Also, if the subject suffers a severe fall (for example
by which they get confused or even worse if they are knocked
unconscious), the subject might be unable to push the button, which
might mean that help doesn't arrive for a significant period of
time, particularly if the subject lives alone. The consequences of
a fall can become more severe if the subject stays lying for a long
time.
Thus the PHBs can include one or more sensors, for example an
accelerometer (usually an accelerometer that measures acceleration
in three dimensions) and an air pressure sensor (for measuring the
height, height change or absolute altitude of the PHB), and the
output of the sensors can be processed to determine if the subject
has suffered a fall. This processing can involve inferring the
occurrence of a fall by processing the time series generated by the
accelerometer and air pressure sensor. In general, a fall detection
algorithm tests on one or more features such as, but not limited
to, impact, orientation, orientation change, height change, and
vertical velocity. Reliable fall detection results when the set of
computed values for these features is different for falls than for
other movements that are not a fall. On detecting a fall, an alarm
is triggered by the PHB without the subject having to press the
button.
Effort is being put into providing robust classification methods or
processing algorithms for detecting falls accurately, since,
clearly, it is important to correctly identify a fall by the
subject so that assistance can be provided, and the occurrence of
false alarms (FA) should be minimised (or even prevented
altogether). Thus automatic fall detection algorithms are optimised
to trade false alarms against the fall detection probability.
However, a problem with achieving reliable fall detection is that
not all falls are the same and different types of falls can have
different features. Usually the optimisation of fall detection
algorithms mean that falls from stance (i.e. fall from a
standing/upright posture) are reliably detected, but this means
that falls from lower positions or involving composite movements
might be missed. Examples include falling from a chair, falling out
of bed, falling when trying to stand up or when trying to sit down.
Falls can also be staged, in the sense that the subject does not
fall straight to the ground, but, for example, the subject slides
down the wall, grasps some furniture (e.g. a table, chair, bed,
etc.), or falls against furniture. These issues with reliable fall
detection are particularly important for subjects that use
wheelchairs, and have additional risk of falling when getting into
or out of their wheelchair.
SUMMARY OF THE INVENTION
A current trend is for the home or care environment to various
include sensors for monitoring the home environment or particular
objects in that environment. These sensors are increasingly
`connected` in the sense that the sensor measurements or products
of the analysis of sensor measurements can be communicated to other
devices (e.g. a remote server, a central home monitoring system, a
smartphone, etc.) via wired or wireless connections through a local
network or over the Internet. These connected sensors are often
referred to as the Internet of Things (IoT) or Internet of Medical
Things (IoMT). Since these sensors may monitor where the subject is
in the environment, what the subject is doing (e.g. which object
the subject is using), etc., the sensors may have information that
is useful to a fall detection algorithm (that typically operates on
measurements of the movements of the subject) to optimise the fall
detection decisions.
However, given the vast array of different sensor types that can be
present in a home or care environment, it will be difficult to
integrate measurements from the sensors actually present in the
environment in a fall detection algorithm implemented by a PHB or
other dedicated fall detector. One way to achieve the integration
is for the PHB or other dedicated fall detector to include a
discovery and communication protocol for connecting to any possible
sensor that is available in the home or care environment. The PHB
or other dedicated fall detector would need to understand all
possible configurations, sensor types, formats and protocols.
Maintenance and flexibility of the system would be difficult in
this architectural configuration and subjects may face the
disappointing experience that adding another sensor in the home
environment that could be used in the fall detection might be
difficult, or even impossible since it is not supported by their
PHB/fall detector software version. Also this type of installation
or set up of the system will be difficult for elderly subjects (the
typical users of fall detectors).
Therefore, there is a need for an improved fall detection
apparatus, method of detecting a fall by a subject and a computer
program product for implementing the method that can make use of
information obtained by sensors in the environment of the subject
to improve the reliability of fall detection, and in particular
improving the reliability of the detection of different types of
falls.
According to a first specific aspect, there is provided a fall
detection apparatus, the fall detection apparatus comprising one or
more processing units configured to obtain a first input indicating
which one or ones of a plurality of fall detection algorithms have
detected a potential fall by the subject, wherein each fall
detection algorithm of the plurality of fall detection algorithms
is associated with a respective type of fall and detects a
potential fall of the associated type by analysing a set of
movement measurements for the subject, wherein each respective type
of fall has an associated initial state of the subject; obtain a
second input indicating the status of the subject prior to the
potential fall, wherein the status of the subject is determined by
analysing a set of measurements from one or more sensors in the
environment of the subject; compare the determined status of the
subject prior to the potential fall to the initial state for each
type of fall associated with any potential fall indicated in the
first input; and output an indication that the subject has fallen
if the determined status of the subject matches the initial state
of any of the respective types of fall associated with any
potential fall indicated in the first input. Thus, the first aspect
provides that information obtained by sensors in the environment of
the subject can be used to determine if a potential fall detected
by one or more fall detection algorithms adapted for respective
types of fall is an actual fall. This improves the reliability of
detection of different types of falls.
In some embodiments, the one or more processing units are further
configured to determine that the subject has not fallen if the
determined status of the subject does not match the initial state
for any of the respective types of fall associated with any
potential fall indicated in the first input. This means that
potential falls identified by a particular fall detection algorithm
(associated with a type of fall) can be disregarded where the
subject was not in the correct initial state for that type of fall
to have occurred.
In some embodiments, the one or more processing units are further
configured such that an indication that the subject has fallen is
not output if the determined status of the subject does not match
the initial state for any of the respective types of fall
associated with any potential fall indicated in the first input.
This means that a care provider or other responder to a fall is not
alerted unless the subject is determined to have fallen.
In some embodiments, the initial state of the subject associated
with a type of fall comprises any one or more of: (i) a standing
posture, (ii) a seated posture, and (iii) a lying posture.
In some embodiments, the respective types of fall associated with
the plurality of fall detection algorithms comprise any one or more
of: (i) a fall from a standing posture, (ii) a fall from a seated
posture, (iii) a fall from a lying posture, (iv) a fall when moving
from a seated posture to a standing posture, (v) a fall when moving
from a standing posture to a sitting posture, (vi) a fall from a
standing posture onto furniture, (vii) a fall from a standing
posture in which the subject slides down a wall.
In some embodiments, the one or more processing units are
configured to obtain the first input by analysing a set of movement
measurements for a subject using the plurality of fall detection
algorithms to detect whether there has been a potential fall by the
subject of the respective type associated with each fall detection
algorithm; and forming the first input from the result of the
analysis of the set of movement measurements using the plurality of
fall detection algorithms. This has the advantage that the fall
detection algorithms and the comparison with the status of the
subject can be performed in the same apparatus, so a separate fall
detection device is not required. In these embodiments, the one or
more processing units can be further configured to receive the set
of movement measurements for the subject from one or more sensors
that are carried or worn by the subject.
In these embodiments, the set of movement measurements can relate
to a first time period, and wherein the one or more processing
units are configured to use the plurality of fall detection
algorithms to analyse the set of movement measurements to detect
whether there has been a potential fall by the subject of the
associated type in the first time period. This means that the fall
detection algorithms all operate on the same movement measurements
to identify falls of the associated types, i.e. each set of
movement measurements is evaluated for each of the different types
of fall.
In some embodiments, each fall detection algorithm in the plurality
of fall detection algorithms can comprise a first fall detection
algorithm having a respective threshold or set of thresholds for
detecting a potential fall of the associated type. In these
embodiments, the first fall detection algorithm can comprise a log
likelihood ratio, LLR, table. In these embodiments each fall
detection algorithm in the plurality of fall detection algorithms
can correspond to a respective point in a receiver-operating
characteristic, ROC, curve for the first fall detection algorithm.
In alternative embodiments, each fall detection algorithm in the
plurality of fall detection algorithms can comprise a respective
set of parameters to be analysed from the set of movement
measurements.
In alternative embodiments, the one or more processing units are
configured to obtain the first input from a fall detection device
that is carried or worn by the subject. These embodiments have the
advantage that the fall detection apparatus can operate with an
existing fall detection device.
In some embodiments, the indication is a fall alert and the
indication is output to a call centre or a care provider
device.
In some embodiments, the one or more processing units are
configured to obtain the second input by analysing a set of
measurements from one or more sensors in the environment of the
subject to determine the status of the subject prior to a potential
fall; and form the second input from the result of the analysis of
the set of measurements from one or more sensors in the environment
of the subject. This has the advantage that the status
determination and the comparison with the output of a plurality of
fall detection algorithms can be performed in the same apparatus,
so a separate monitoring system is not required.
In alternative embodiments, the one or more processing units are
configured to obtain the second input from a monitoring system that
includes the one or more sensors in the environment of the subject.
These embodiments have the advantage that the fall detection
apparatus can be used with an existing monitoring system.
In some embodiments, the one or more sensors in the environment of
the subject comprise one or more of (i) a sensor for measuring
whether the subject is using an item of furniture; (ii) a sensor
for measuring whether the subject is using a wheelchair; (iii) a
sensor to measuring whether the subject is in a room; and (iv) a
sensor for measuring whether an object in the environment is being
used.
In some embodiments, the status of the subject comprises any one or
more of (i) sitting on a chair or bed, (ii) lying on a bed, (iii)
walking or standing, (iv) sitting in a wheelchair, (v) about to get
into a wheelchair.
According to a second specific aspect, there is provided a method
of detecting a fall, the method comprising obtaining a first input
indicating which one or ones of a plurality of fall detection
algorithms have detected a potential fall by the subject, wherein
each fall detection algorithm of the plurality of fall detection
algorithms is associated with a respective type of fall and detects
a potential fall of the associated type by analysing a set of
movement measurements for the subject, wherein each respective type
of fall has an associated initial state of the subject; obtaining a
second input indicating the status of the subject prior to the
potential fall, wherein the status of the subject is determined by
analysing a set of measurements from one or more sensors in the
environment of the subject; comparing the determined status of the
subject prior to the potential fall to the initial state for each
type of fall associated with any potential fall indicated in the
first input; and outputting an indication that the subject has
fallen if the determined status of the subject matches the initial
state of any of the respective types of fall associated with any
potential fall indicated in the first input. Thus, the second
aspect provides that information obtained by sensors in the
environment of the subject can be used to determine if a potential
fall detected by one or more fall detection algorithms adapted for
respective types of fall is an actual fall. This improves the
reliability of detection of different types of falls.
In some embodiments, the method further comprises determining that
the subject has not fallen if the determined status of the subject
does not match the initial state for any of the respective types of
fall associated with any potential fall indicated in the first
input. This means that potential falls identified by a particular
fall detection algorithm (associated with a type of fall) can be
disregarded where the subject was not in the correct initial state
for that type of fall to have occurred.
In some embodiments, an indication that the subject has fallen is
not output if the determined status of the subject does not match
the initial state for any of the respective types of fall
associated with any potential fall indicated in the first input.
This means that a care provider or other responder to a fall is not
alerted unless the subject is determined to have fallen.
In some embodiments, the initial state of the subject associated
with a type of fall comprises any one or more of: (i) a standing
posture, (ii) a seated posture, and (iii) a lying posture.
In some embodiments, the respective types of fall associated with
the plurality of fall detection algorithms comprise any one or more
of: (i) a fall from a standing posture, (ii) a fall from a seated
posture, (iii) a fall from a lying posture, (iv) a fall when moving
from a seated posture to a standing posture, (v) a fall when moving
from a standing posture to a sitting posture, (vi) a fall from a
standing posture onto furniture, (vii) a fall from a standing
posture in which the subject slides down a wall.
In some embodiments, the step of obtaining the first input
comprises analysing a set of movement measurements for a subject
using the plurality of fall detection algorithms to detect whether
there has been a potential fall by the subject of the respective
type associated with each fall detection algorithm; and forming the
first input from the result of the analysis of the set of movement
measurements using the plurality of fall detection algorithms. This
has the advantage that the fall detection algorithms and the
comparison with the status of the subject can be performed in the
same apparatus, so a separate fall detection device is not
required. In these embodiments, the method can further comprise
receiving the set of movement measurements for the subject from one
or more sensors that are carried or worn by the subject.
In these embodiments, the set of movement measurements can relate
to a first time period, and wherein the step of analysing comprises
using the plurality of fall detection algorithms to analyse the set
of movement measurements to detect whether there has been a
potential fall by the subject of the associated type in the first
time period. This means that the fall detection algorithms all
operate on the same movement measurements to identify falls of the
associated types, i.e. each set of movement measurements is
evaluated for each of the different types of fall.
In some embodiments, each fall detection algorithm in the plurality
of fall detection algorithms can comprise a first fall detection
algorithm having a respective threshold or set of thresholds for
detecting a potential fall of the associated type. In these
embodiments, the first fall detection algorithm can comprise a log
likelihood ratio, LLR, table. In these embodiments each fall
detection algorithm in the plurality of fall detection algorithms
can correspond to a respective point in a receiver-operating
characteristic, ROC, curve for the first fall detection algorithm.
In alternative embodiments, each fall detection algorithm in the
plurality of fall detection algorithms can comprise a respective
set of parameters to be analysed from the set of movement
measurements.
In alternative embodiments, the step of obtaining the first input
comprises obtaining the first input from a fall detection device
that is carried or worn by the subject. These embodiments have the
advantage that the method can operate with an existing fall
detection device.
In some embodiments, the indication is a fall alert and the
indication is output to a call centre or a care provider
device.
In some embodiments, the step of obtaining the second input
comprises analysing a set of measurements from one or more sensors
in the environment of the subject to determine the status of the
subject prior to a potential fall; and forming the second input
from the result of the analysis of the set of measurements from one
or more sensors in the environment of the subject. This has the
advantage that the status determination and the comparison with the
output of a plurality of fall detection algorithms can be performed
in the same apparatus, so a separate monitoring system is not
required.
In alternative embodiments, the step of obtaining the second input
comprises obtaining the second input from a monitoring system that
includes the one or more sensors in the environment of the subject.
These embodiments have the advantage that the method can be used
with an existing monitoring system.
In some embodiments, the one or more sensors in the environment of
the subject comprise one or more of (i) a sensor for measuring
whether the subject is using an item of furniture; (ii) a sensor
for measuring whether the subject is using a wheelchair; (iii) a
sensor to measuring whether the subject is in a room; and (iv) a
sensor for measuring whether an object in the environment is being
used.
In some embodiments, the status of the subject comprises any one or
more of (i) sitting on a chair or bed, (ii) lying on a bed, (iii)
walking or standing, (iv) sitting in a wheelchair, (v) about to get
into a wheelchair.
According to a third aspect, there is provided a computer program
product comprising a computer readable medium having computer
readable code embodied therein, the computer readable code being
configured such that, on execution by a suitable computer or
processor, the computer or processor is caused to perform the
method according to the second aspect or any embodiment
thereof.
According to a fourth aspect, there is provided a fall detection
device, that comprises one or more movement sensors for measuring
the movements of a subject; one or more processing units configured
to receive a set of movement measurements for the subject from the
one or more movement sensors; analyse the set of movement
measurements using a plurality of fall detection algorithms to
detect whether there has been a potential fall by the subject of a
respective type of fall associated with each fall detection
algorithm, wherein each respective type of fall has an associated
initial state of the subject; and form a first input from the
result of the analysis of the set of movement measurements using
the plurality of fall detection algorithms; and a fall detection
apparatus according to the first aspect above. Thus, in this
aspect, the fall detection apparatus, or the functions thereof
defined in the first aspect, are part of, or implemented by, a fall
detection device.
According to a fifth aspect, there is provided a monitoring system
that comprises one or more processing units configured to receive a
set of measurements from one or more sensors in an environment of a
subject; analyse the set of measurements to determine the status of
the subject prior to a potential fall; and form a second input from
the result of the analysis of the set of measurements; and a fall
detection apparatus according to the first aspect above. Thus, in
this aspect, the fall detection apparatus, or the functions thereof
defined in the first aspect, are part of, or implemented by, a
monitoring system.
These and other aspects will be apparent from and elucidated with
reference to the embodiment(s) described hereinafter.
BRIEF DESCRIPTION OF THE DRAWINGS
Exemplary embodiments will now be described, by way of example
only, with reference to the following drawings, in which:
FIG. 1 is a block diagram illustrating an apparatus according to an
exemplary embodiment; and
FIG. 2 is a flow chart illustrating a method according to an
exemplary embodiment.
DETAILED DESCRIPTION OF EMBODIMENTS
As noted above, the invention aims to make use of information
obtained by sensors in the environment of the subject to improve
the reliability of fall detection, and in particular improving the
reliability of the detection of different types of falls, while
minimising the occurrence of false alarms.
Fall detection algorithms can be optimised to detect different
types of fall, but this means that other types of fall might not be
reliably detect by the algorithm. For example an algorithm
optimised to reliably detect falls from a standing posture
(including when walking), might not reliably detect falls when
getting up from a chair, since features characteristic of a fall
from standing might not be present in movement measurements
corresponding to a fall when trying to stand up, and vice
versa.
Thus, movement measurements for a subject can be evaluated by a
number of different fall detection algorithms that are each
optimised for a respective type of fall (e.g. falling from
standing, falling while trying to stand up, etc.), and each
algorithm can provide an output indicating whether or not a fall
has potentially been detected in the movement measurements. It may
be the case that, depending on the particular configuration of the
algorithms and the particular movement measurements, more than one
fall detection algorithm can indicate a fall at a given time.
One way to implement the different fall detection algorithms is to
use the same feature/parameter set (e.g. impact, height change,
orientation change, etc.) and the same log likelihood ratio (LLR)
tables, but each algorithm can use different decision thresholds
for the total LLR value, depending on the type of fall. In other
words, a different operating point on a receiver-operating
characteristic (ROC) curve can be used for each fall detection
algorithm/fall type. As is known, the reliability of a
classification method can be visualised by a ROC curve in which the
detection probability is plotted against the false alarm rate, and
the operating point of an algorithm on the ROC curve can be
selected to achieve a required detection probability or false alarm
rate. As is known from Detection Theory, an optimal detector is
found by testing the so-called likelihood ratio. This ratio
expresses the probability on a given feature value (for example,
size of impact) in case of a fall divided by the probability on
that given feature value in case of a non-fall (i.e. any movement
giving rise to the same number but not being a fall). The larger
this ratio the more likely the observed event (impact, in the
example) is due to a fall. Comparison to a set (by design)
threshold enables the detector to conclude that the event is a fall
or is not a fall. The likelihood ratio for a range of feature
values (impact sizes, in the example) is commonly stored in a
table. For ease of computation, the logarithm of the ratio is
stored rather than the ratio itself.
Another way to implement the different fall detection algorithms is
to, for example, use a different set of features/parameters for one
or more of the fall detection algorithms that are appropriate for
the type of fall that is to be detected. For example, the set of
parameters used by a fall detection algorithm to detect falls when
the subject is close to or seated in a chair (including a
wheelchair), may be different to the set of parameters used by a
fall detection algorithm to detect falls when the subject is
walking. Example features/parameters that can be used include the
time window over which a height change is computed, the required
height change over the event, and the decision threshold of the
overall likelihood between falls and non-falls. Alternatively or in
addition, the LLR table used by each algorithm can also be
different, with the LLR table fitting to the distribution
corresponding to the associated fall type. For example, the LLR
table for the height change when falling from a chair may have its
largest likelihood at a lower height change compared to the LLR
table for falls from stance. Similarly, the impact and/or
orientation LLR tables can reflect different log likelihood values.
It may also or alternatively be the case that the way in which the
features/parameters are computed is different between the different
algorithms, for example using different signal processing
techniques.
As noted above, it is desirable to be able to make use of the
information available from one or more sensors in the home
environment, for example sensors that are part of a home monitoring
system. Therefore, the status of the subject that can be derived
from measurements from the environment sensor(s) can be used to
`filter` or `validate` the output of any fall detection algorithm
that indicates that a potential fall may have taken place. For
example, based on a set of movement measurements, a fall detection
algorithm optimised for detecting falling out of bed may indicate
that the subject may have fallen (with the fall detection
algorithms optimised for other types of fall not indicating a
potential fall), but the status of the subject derived from the
environment sensor(s) may indicate that the subject is walking
around the house (and that the subject was not in bed at the time
the potential fall was indicated). In that case, it is possible to
dismiss or ignore the potential fall indicated by the
falling-out-of-bed-optimised fall detection algorithm as it is not
consistent with the current status of the subject provided by the
environment sensor(s). On the other hand, if the environment
sensor(s) indicated that the subject was in bed at the time (and/or
prior to the time) that the potential fall was detected, then the
potential fall is consistent with the status of the subject, and a
fall can be positively detected (with an alarm being triggered
and/or an alert being sent).
In a particular embodiment of the invention, a fall detection
device (e.g. a personal help button (PHB) that includes one or more
movement sensors) that is carried or worn by a subject can evaluate
movement measurements using a range of fall detection algorithms,
with each algorithm deciding, for a given (triggered) event (i.e.
set of movement measurements meeting some trigger condition),
whether the event is a fall assuming a certain situation (e.g. a
fall from stance, a fall from a chair, a fall from a bed, etc.).
The algorithms may share computation components, i.e. the
algorithms can be evaluated by the same processing unit in the fall
detection device.
In some embodiments, a first part of the analysis of the movement
measurements may be common to all of the fall detection algorithms,
with the individual fall detection algorithms being used if a
trigger condition is met. Alternatively, a first part of the
analysis may be different for different fall detection algorithms.
In either case, the movement measurements (e.g. acceleration, air
pressure, etc.) are received and a test can be run on the
measurements to determine whether the trigger condition is met. For
example, it can be tested whether the air pressure has risen
relative to the air pressure some time period (e.g. 2 seconds)
earlier by an amount larger than an air pressure change equivalent
to a predetermined height change (e.g. 50 cm). An accelerometer
based trigger condition could observe an orientation change in a
similar fashion, or observe for an impact (e.g. the magnitude of
the norm of the accelerometer signals exceeds some threshold). If
in this way a trigger happens (i.e. the trigger condition is met),
the segment of movement measurements (i.e. segment of a movement
measurement signal) around the time that trigger condition was met
is forwarded for further processing. In this way the use of the
trigger condition converts the (potentially continuous) sensor
signals/measurements into a sequence of (discrete) events. The
trigger condition should require low complexity and low power
consumption to evaluate. It should pass all `true` falls and pass
as few `non-falls` as possible (although it will be appreciated
that the main suppression of non-falls is the task of the
subsequent fall detection algorithms, but the rate of these
non-fall events sets the calling rate of the fall detection
device).
In case one or more of the algorithms decides the event is a fall,
each positive decision (i.e. detected fall) can be communicated
(e.g. transmitted) to a central console (referred to as a fall
detection apparatus below) in the home or care environment. Each
positive decision can be labelled with the type of
algorithm/situation that produced the positive decision (i.e. a
fall from stance, a fall from a chair, a fall from a bed,
etc.).
The central console can be connected to (or at least able to
receive information from) a pre-existing home or care environment
monitoring system (for example a burglar surveillance system, a
fire/smoke detection system, and/or an activities of daily living
(ADL) monitoring system). The monitoring system implements and
handles the discovery and communication with any environmental
sensors in the home or care environment (thereby avoiding any need
for the fall detection device or central console to do that). The
monitoring system can also implement and execute algorithms that
analyse the environmental sensor measurements to determine the
status of the subject in the home or care environment. This status
is provided to the central console.
The environment sensors can include sensors that can be placed at
or on furniture, or otherwise be associated with items of
furniture, such as a chair, a couch, a bed, a cupboard, a shower,
at a bed side cabinet, etc. These sensors can be used to measure
whether the subject is using the particular item of furniture
and/or is near to the particular item of furniture.
When the central console receives an indication of a detected fall
by the fall detection device and the associated fall-type label(s),
the console tests whether that fall type coincides with the
situation as currently inferred by the monitoring system. If so, an
alarm that the subject has fallen is forwarded to a call centre or
other help providing entity (e.g. the emergency services). In some
implementations, if the fall detection algorithm for detecting a
fall from stance (i.e. standing) detects a potential fall, an alert
or alarm may always be triggered (e.g. it can be excluded from the
test against the current status, or a mismatch with the current
status may be ignored).
In another particular embodiment of the invention (which can be
used in combination with or separately from the home monitoring
system used in the above particular embodiment), an environment
sensor can be provided to detect when a subject is sitting in a
wheelchair, and/or is about to be seated in a wheelchair (i.e. the
sensor can be used to detect if the subject is standing in front of
the wheelchair). Examples of such sensors include passive infrared
(PIR) sensors, ultrasound (US) sensors, radar-based sensors,
near-field communication (NFC) sensors, pressure sensors (i.e. for
detecting pressure or force applied to part of the wheel chair,
e.g. the seat portion and/or handles/hand grips), light sensors
(e.g. photodiodes) for sensing a light beam from, e.g. a laser or
light emitting diode, LED, etc. A fall detection algorithm can be
provided or used that evaluates whether a fall from a wheelchair
has occurred (either from the wheelchair or when trying to sit down
in, and/or get up from, the wheelchair). A positive fall indication
from the fall detection algorithm can be compared to measurements
from the environment sensor associated with the wheelchair, and a
fall detected if the subject was sat in or close to the wheelchair
at a time corresponding to the time at which the fall was detected
by the algorithm.
In some embodiments, if the wheelchair is an electric wheelchair
and/or otherwise has an electronically actuated brake (for
preventing movement of the wheelchair), the brake can be
automatically actuated to prevent movement of the wheelchair if the
environment sensor detects that the subject is standing in front of
the wheelchair. If the sensor (or another) detects that the subject
has sat down in the wheelchair, then the brake can be released
(unless manually applied by the subject).
It will be appreciated that in some implementations the environment
sensors can be operating continuously or periodically to monitor
the environment/subject, in which case the status of the subject
may be determined continuously or periodically. Alternatively, the
environment sensors can be operating continuously or periodically
to monitor the environment/subject, but the processing to determine
the status of the subject may only be performed when required (e.g.
following receipt of a positive fall indication from one or more
fall detection algorithms). As another alternative, the environment
sensors may only measure the environment/subject when requested to
do so (e.g. following receipt of a positive fall indication from
one or more fall detection algorithms). This alternative reduces
the energy consumption of the system.
FIG. 1 illustrates an exemplary fall detection apparatus 2 that can
be used to implement various embodiments of the invention. The
apparatus 2 is shown as part of a system 4 that includes one or
more movement sensors 6 that are provided to measure the movements
of a subject and one or more environment sensors 8 that are
provided to measure an aspect of the environment of the subject.
The fall detection apparatus 2 is provided for detecting if a
subject has fallen by comparing a status of the subject prior to a
potential fall (as determined from measurements from the
environment sensor(s) 8) to an initial state for a type of fall
associated with any fall detection algorithm that has detected a
potential fall by the subject (as determined from measurements from
the movement sensor(s) 6), and outputting an indication that the
subject has fallen if there is a match between the status and an
initial state. As such, the fall detection apparatus 2 can also be
referred to as a fall decision apparatus 2 since it takes a final
decision on whether a fall has occurred and an alarm should be
triggered or an alert issued.
In some embodiments, the measurements from the movement sensor(s) 6
are provided to the fall detection apparatus 2, and the fall
detection apparatus 2 analyses the movement measurements using a
plurality of fall detection algorithms to detect a potential fall
by the subject. In other embodiments, the movement sensor(s) 6 can
be integral with the fall detection apparatus 2. In this case, the
fall detection apparatus 2 can be worn or carried by the subject,
and may be in the form of a watch, bracelet, necklace, chest band,
etc. In other embodiments, the movement sensor(s) 6 are part of a
separate fall detection device 10 (indicated by dashed box 10
around the movement sensor(s) 6), and the fall detection device 10
applies the fall detection algorithms to the movement measurements
to detect a potential fall by the subject. The fall detection
device 10 can be carried or worn by the subject, and can, for
example, include a PHB. The fall detection device 10 can be in the
form of a watch, bracelet, necklace, chest band, etc. It will be
appreciated that the fall detection device 10, where present,
merely provides an input to the fall detection apparatus 2
indicating the outcome of the analysis of the movement measurements
by the plurality of fall detection algorithms. The fall detection
apparatus 2 determines whether a fall alert should be issued based
on a comparison of the fall detection algorithm results with the
status of the subject determined from the environment sensor(s) 8.
In some alternative embodiments, the functions of the fall
detection apparatus 2 described herein are part of, or implemented
by, the fall detection device 10. In these embodiments, the fall
detection device 2 can be worn or carried by the subject, and may
be in the form of a watch, bracelet, necklace, chest band, etc.,
and may include or be connected to the movement sensor(s) 6.
In some embodiments, the measurements from the environment
sensor(s) 8 are provided to the fall detection apparatus 2, and the
fall detection apparatus 2 analyses the measurements to determine a
status of the subject. In other embodiments, one or more of the
environment sensor(s) 8 can be integral with the fall detection
apparatus 2 (with optionally other environment sensor(s) 8 being
separate from the fall detection apparatus 2). In other
embodiments, the environment sensor(s) 8 are part of a monitoring
system 12 (indicated by dashed box 12 around the environment
sensor(s) 8). In some alternative embodiments, the functions of the
fall detection apparatus 2 described herein are part of, or
implemented by, the monitoring system 12.
It will be appreciated that various combinations of the embodiments
in the preceding two paragraphs is possible. For example, the fall
detection apparatus 2 can perform all of the processing of the
sensor measurements (e.g. analysis of the movement measurements
received from the movement sensor(s) 6 using a plurality of fall
detection algorithms and analysis of the environment sensor
measurements received from the environment sensor(s) 8 (where one
of the movement sensor(s) 6 and environment sensor(s) 8 may be
integral with the fall detection apparatus 2) to determine the
status of the subject), perform none of the processing of the
sensor measurements (e.g. the fall detection apparatus 2 receives
the result of the fall detection algorithm analysis from fall
detection device 10 and receives the status of the subject from the
monitoring system 12), or perform the processing of one set of
sensor measurements while receiving the result of the processing of
the other set of sensor measurements. In any of the above
embodiments, the one or more movement sensors 6 are carried or worn
by the subject, and the one or more environment sensors 8 are
located in the environment of the subject (i.e. they are not worn
or carried by the subject).
The fall detection apparatus 2 includes a processing unit 14 that
controls the operation of the fall detection apparatus 2 and that
can be configured to execute or perform the methods described
herein. The processing unit 14 can be implemented in numerous ways,
with software and/or hardware, to perform the various functions
described herein. The processing unit 14 may comprise one or more
microprocessors or digital signal processor (DSPs) that may be
programmed using software or computer program code to perform the
required functions and/or to control components of the processing
unit 14 to effect the required functions. The processing unit 14
may be implemented as a combination of dedicated hardware to
perform some functions (e.g. amplifiers, pre-amplifiers,
analog-to-digital convertors (ADCs) and/or digital-to-analog
convertors (DACs)) and a processor (e.g., one or more programmed
microprocessors, controllers, DSPs and associated circuitry) to
perform other functions. Examples of components that may be
employed in various embodiments of the present disclosure include,
but are not limited to, conventional microprocessors, DSPs,
application specific integrated circuits (ASICs), and
field-programmable gate arrays (FPGAs).
The processing unit 14 is connected to a memory unit 16 that can
store data, information and/or signals for use by the processing
unit 14 in controlling the operation of the fall detection
apparatus 2 and/or in executing or performing the methods described
herein. In some implementations the memory unit 16 stores
computer-readable code that can be executed by the processing unit
14 so that the processing unit 14 performs one or more functions,
including the methods described herein. The memory unit 16 can
comprise any type of non-transitory machine-readable medium, such
as cache or system memory including volatile and non-volatile
computer memory such as random access memory (RAM) static RAM
(SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable
ROM (PROM), erasable PROM (EPROM) and electrically erasable PROM
(EEPROM), implemented in the form of a memory chip, an optical disk
(such as a compact disc (CD), a digital versatile disc (DVD) or a
Blu-Ray disc), a hard disk, a tape storage solution, or a solid
state device, including a memory stick, a solid state drive (SSD),
a memory card, etc.
The fall detection apparatus 2 also includes interface circuitry 18
for enabling a data connection to and/or data exchange with other
devices, including any one or more of servers, databases, user
devices, and sensors. The connection may be direct or indirect
(e.g. via the Internet), and thus the interface circuitry 18 can
enable a connection between the fall detection apparatus 2 and a
network, such as the Internet, via any desirable wired or wireless
communication protocol. For example, the interface circuitry 18 can
operate using WiFi, Bluetooth, Zigbee, or any cellular
communication protocol (including but not limited to Global System
for Mobile Communications (GSM), Universal Mobile
Telecommunications System (UMTS), Long Term Evolution (LTE),
LTE-Advanced, etc.). In the case of a wireless connection, the
interface circuitry 18 (and thus fall detection apparatus 2) may
include one or more suitable antennas for transmitting/receiving
over a transmission medium (e.g. the air). Alternatively, in the
case of a wireless connection, the interface circuitry 18 may
include means (e.g. a connector or plug) to enable the interface
circuitry 18 to be connected to one or more suitable antennas
external to the fall detection apparatus 2 for
transmitting/receiving over a transmission medium (e.g. the air).
The interface circuitry 18 is connected to the processing unit
14.
The interface circuitry 18 can be used to receive movement
measurements from the movement sensor(s) 6 or, where the movement
sensor(s) 6 are part of a fall detection device 10, the interface
circuitry 18 can be used to receive the result of the analysis of
movement measurements by a plurality of fall detection algorithms.
The interface circuitry 18 can also be used to receive measurements
from the environment sensor(s) 8, or, where the environment
sensor(s) 8 are part of a monitoring system 12, the interface
circuitry 18 can be used to receive the determined status of the
subject.
The interface circuitry 18 can also be used to output an indication
that the subject has fallen. In that case, the interface circuitry
18 can communicate the indication to a call centre or the emergency
services and/or communicate the indication to a user device of a
physician or care provider.
In some embodiments, the fall detection apparatus 2 comprises a
user interface 20 that includes one or more components that enables
a user of fall detection apparatus 2 (e.g. the subject, or a care
provider for the subject) to input information, data and/or
commands into the fall detection apparatus 2, and/or enables the
fall detection apparatus 2 to output information or data to the
user of the fall detection apparatus 2. An output may be an audible
alarm or alert that the subject has fallen. The user interface 20
can comprise any suitable input component(s), including but not
limited to a keyboard, keypad, one or more buttons, switches or
dials, a mouse, a track pad, a touchscreen, a stylus, a camera, a
microphone, etc., and the user interface 20 can comprise any
suitable output component(s), including but not limited to a
display screen, one or more lights or light elements, one or more
loudspeakers, a vibrating element, etc.
The fall detection apparatus 2 can be any type of electronic device
or computing device. For example the fall detection apparatus 2 can
be, or be part of, a server, a computer, a laptop, a tablet, a
smartphone, a smartwatch, etc.
It will be appreciated that a practical implementation of a fall
detection apparatus 2 may include additional components to those
shown in FIG. 1. For example the fall detection apparatus 2 may
also include a power supply, such as a battery, or components for
enabling the fall detection apparatus 2 to be connected to a mains
power supply.
In embodiments where the movement sensor(s) 6 are part of a fall
detection device 10, the fall detection device 10 may include a
processing unit (shown by dashed box 22) for analysing the movement
measurements using the plurality of fall detection algorithms and
determining whether the subject has potentially suffered a fall.
The fall detection device 10 may also include interface circuitry
(shown by dashed box 24) for enabling the result of the analysis of
the movement measurements to be communicated to the fall detection
apparatus 2. The processing unit 22 and/or interface circuitry 24
may be implemented in similar ways to the processing unit 14 and/or
interface circuitry 18 in the fall detection apparatus 2.
In embodiments where the environment sensor(s) 8 are part of a
monitoring system 12, the monitoring system 12 may include a
processing unit (shown by dashed box 26) for analysing the
environment sensor measurements and determining the status of the
subject. The monitoring system 12 may also include interface
circuitry (shown by dashed box 28) for enabling the determined
status to be communicated to the fall detection apparatus 2. The
processing unit 26 and/or interface circuitry 28 may be implemented
in similar ways to the processing unit 14 and/or interface
circuitry 18 in the fall detection apparatus 2.
The one or more movement sensor(s) 6 can include any type of
sensor(s) for measuring the movements of a subject, or for
providing measurements representative of the movements of a
subject. For example, the movement sensor(s) 6 can include any one
or more of an accelerometer, a magnetometer, a satellite
positioning system receiver (e.g. a GPS receiver, a GLONASS
receiver, a Galileo positioning system receiver), a gyroscope, and
an air pressure sensor (that can provide measurements indicative of
the altitude of the subject or changes in height/altitude of the
subject).
The one or more environment sensor(s) 8 can include any type of
sensor(s) for monitoring an aspect of an environment or an aspect
of an object in an environment. For example, the environment
sensor(s) 8 can include one or more sensors 8 for detecting whether
the subject is using an item of furniture, one or more sensors 8
for measuring or detecting whether the subject is using a
wheelchair, one or more sensors 8 for measuring whether the subject
is in a particular room, and/or one or more sensors 8 for measuring
whether an object in the environment is being used. The environment
sensor(s) 8 may be or include any one or more of an accelerometer,
a gyroscope, a PIR sensor, an US sensor, a radar-based sensor, a
light-based sensor, a radio frequency (RF) signal-based sensor
(e.g. using WiFi, Bluetooth, Zigbee, etc.) from which signal
strength measurements can be obtained, an NFC sensor, a pressure
sensor (i.e. for detecting pressure or force applied to part of an
object), a camera, etc.
In some embodiments, in addition to the movement sensor(s) 6, one
or more physiological characteristic sensors can be provided for
monitoring or measuring physiological characteristics of the
subject, and these physiological characteristic measurements can be
evaluated as part of the fall detection algorithm(s). For example,
physiological characteristics such as heart rate, skin
conductivity, breathing rate, blood pressure and/or body
temperature can vary following a fall, and therefore an evaluation
of these measurements can provide useful information for
determining whether a subject has fallen. The one or more
physiological characteristic sensors can include a
photoplethysmograph (PPG) sensor that can measure heart rate, heart
rate-related characteristics and breathing rate, a skin
conductivity sensor, blood pressure monitor, thermometer, etc.
It will be appreciated that where an environmental sensor 8 is for
monitoring a particular object (e.g. a particular item of
furniture), the environment sensor(s) 8 may include respective
environment sensors 8 for monitoring respective items of furniture
(e.g. a respective pressure sensor can be provided on each chair in
the environment). Likewise, where an environmental sensor 8 is for
monitoring the presence of the subject in a particular room, the
environment sensor(s) 8 may include respective environment sensors
8 for monitoring respective rooms (e.g. a respective PIR sensor can
be provided in a bedroom, kitchen, bathroom, etc.
The flow chart in FIG. 2 illustrates an exemplary method according
to the techniques described herein. One or more of the steps of the
method can be performed by the processing unit 14 in the apparatus
2, in conjunction with any of the memory unit 16, interface
circuitry 18 and user interface 20 as appropriate. The processing
unit 14 may perform the one or more steps in response to executing
computer program code, that can be stored on a computer readable
medium, such as, for example, the memory unit 16.
In a first step, step 101, the processing unit 14 obtains an input
(referred to for clarity as a "first" input) indicating which one
or ones of a plurality of fall detection algorithms have detected a
potential fall by the subject. Each fall detection algorithm is
associated with a respective type of fall and detects a potential
fall of the associated type by analysing a set of movement
measurements for the subject. Each respective type of fall has an
associated initial state of the subject, i.e. a posture or state of
the subject immediately before the fall.
Some exemplary types of fall that can be detected using respective
fall detection algorithms and their respective initial states
include (but are not limited to) any one or more of a fall from a
standing posture, including when walking, jogging or running (with
the initial state being a standing posture), a fall from a seated
posture (with the initial state being a seated posture), a fall
from a lying posture (with the initial state being a lying
posture), a fall when moving from a seated posture to a standing
posture (with the initial state being a seated posture), a fall
when moving from a standing posture to a sitting posture (with the
initial state being a standing posture), a fall from a standing
posture onto furniture (with the initial state being a standing
posture), and a fall from a standing posture in which the subject
slides down a wall (with the initial state being a standing
posture).
In some embodiments, step 101 comprises obtaining the first input
from a fall detection device 10 that is carried or worn by the
subject.
In alternative embodiments, step 101 comprises the processing unit
14 determining the first input by analysing a set of movement
measurements from the movement sensor(s) 6 using the plurality of
fall detection algorithms to detect whether there has been a
potential fall by the subject of the respective type associated
with each fall detection algorithm. The first input can be formed
from the result of the analysis of the set of movement measurements
using the plurality of fall detection algorithms. In this
embodiment, the processing unit 14 can receive the set of movement
measurements are obtained using the movement sensor(s) 6.
In either embodiment of step 101, the set of movement measurements
relate to a first time period, and the plurality of fall detection
algorithms are used (either by a fall detection device 10 or the
processing unit 14) to analyse the set of movement measurements to
detect whether there has been a potential fall by the subject of
the associated type in the first time period. That is, the
plurality of fall detection algorithms are used to evaluate the
same time period of measurements for a potential fall.
In some embodiments, each fall detection algorithm in the plurality
of fall detection algorithms use the same (shared) fall detection
algorithm (e.g. extracted feature sets), but have a respective
threshold or set of thresholds for detecting a potential fall of
the associated type. The shared fall detection algorithm can
comprise a LLR table. Each fall detection algorithm in the
plurality can correspond to a respective point in a ROC for the
shared fall detection algorithm.
Alternatively, each fall detection algorithm in the plurality of
fall detection algorithms can comprise a respective set of
parameters or features to be analysed or extracted from the set of
movement measurements.
It will be appreciated that each fall detection algorithm can be
trained or configured based on known falls of the appropriate type.
For example the parameters, features, LLR table and/or thresholds
of a fall detection algorithm for detecting a fall from a lying
posture can be trained based on movement measurements from known
falls from a bed.
Next, in step 103, the processing unit 14 obtains an input
(referred to for clarity as a "second" input) indicating the status
of the subject prior to a potential fall. The status of the subject
is determined from an analysis of a set of measurements from one or
more environmental sensors 8 in the environment of the subject.
Step 103 can comprise obtaining the second input from a monitoring
system 12 that includes the one or more sensors 8 in the
environment of the subject.
Alternatively, step 103 can comprise the processing unit 14
receiving a set of measurements from the one or more sensors 8 in
the environment of the subject, analysing the set of measurements
from the one or more sensors 8 to determine the status of the
subject prior to a potential fall and forming the second input from
the result of the analysis of the set of measurements from one or
more sensors in the environment of the subject.
The status of the subject indicated in the second input can
comprise any one or more of sitting on a chair or bed, lying on a
bed, walking (including jogging or running) or standing, sitting in
a wheelchair, and about to get into a wheelchair.
Techniques for analysing environmental sensor measurements to
determine a current status of a subject, such as their location
(the room they are in), an object that the subject is using (e.g.
sitting in a chair, pouring a kettle, etc.) are known in the art,
and details are not provided herein. Such processing techniques are
known, for example, in terms of systems and devices that determine
the activities of daily living (ADL) of a subject. In any case, it
will be appreciated that many such processing techniques can be
straightforward to implement. For example if a pressure sensor on a
chair indicates a person is sitting on the chair, then it can be
inferred that the subject is sitting on the chair associated with
that pressure sensor. In a similar example, several pressure
sensors can be provided at different positions on a bed, and a high
pressure measured by one sensor can indicate that the subject is
sitting on the bed, and a high pressure measured by several sensors
can indicate that the subject is lying on the bed. If the subject
is detected as being in a living room and the television is
switched on, it could be inferred that the subject is sitting
down.
In step 105, the determined status of the subject prior to a
potential fall (from the second input) is compared to the initial
state for each type of fall associated with any potential fall
indicated in the first input. That is, for any type of fall
indicated in the first input, the initial state is compared to the
determined status of the subject.
Then, in step 107, if the determined status of the subject matches
the initial state of any of the respective types of fall associated
with any potential fall indicated in the first input, then a fall
is detected and an indication that a fall has occurred is output by
the fall detection apparatus 2. The indication can be a fall alert.
For example, if the first input indicates two potential falls, with
one potential fall being from a fall detection algorithm that
evaluates for falls when moving from a standing posture to a
sitting posture, and the other potential fall being from a fall
detection algorithm that evaluates for falls from a seated posture,
and the determined status prior to the potential fall was that the
subject was sitting on a chair, then a match has occurred, and a
fall from a seated posture is identified. The indication may be
output in the form of an audible alarm, a visible message or light,
or a signal that is transmitted to a care provider device,
physician device, call centre or emergency service.
If in step 105 the determined status of the subject does not match
the initial state for any of the respective types of fall
associated with any potential fall indicated in the first input,
then the processing unit 14 determines that the subject has not
fallen. In this case, no indication that the subject has fallen is
output. In the above example, if the determined status prior to the
potential fall was that the subject was lying on a bed, then there
is no match, and no fall is detected.
Thus, the above method provides a number of improvements to the
reliability of fall detection. Firstly, the different fall
detection algorithms can each be optimised for detecting a
respective type of fall (e.g. falling from standing, falling while
trying to stand up, etc.), increasing the chance of successfully
detecting a particular type of fall. However in recognition of
these optimised fall detection algorithms having a higher false
alarm rate in circumstances where the subject is not in the
appropriate initial state (e.g. the output of a fall-from-standing
detection algorithm will be less reliable if the subject is lying
down rather than standing), the status of the subject is determined
using sensors in the environment of the subject and used to check
whether any indicated potential fall is plausible. Thus in the case
where one of the plurality of fall detection algorithms has
detected a potential fall by the subject, the status of the subject
prior to the potential fall is checked against the initial state
associated with the fall detection algorithm that detected the
potential fall to make sure that the potential fall was plausible
given the status of the subject prior to the potential fall being
detected. Using the status of the subject therefore acts as a check
against a positive detection of a potential fall, thereby improving
the reliability of the fall detection. In the case where multiple
ones of the plurality of fall detection algorithms indicate a
potential fall by the subject, the status of the subject prior to
the potential fall is checked against the initial state associated
with the multiple fall detection algorithms that detected the
potential fall to determine whether any of the potential falls is
plausible given the status of the subject prior to the detected
potential fall. The use of the status of the subject therefore acts
as a check against a positive detection of a potential fall by
multiple fall detection algorithms, and, if a fall has occurred,
enables a more reliable detection of the type of fall. In either of
the above examples, if the status of the subject does not match the
detection of a potential fall by a particular fall detection
algorithm, then that potential fall can be dismissed as a false
alarm as it is inconsistent with the initial state of the subject
(and likely triggered by that fall detection algorithm being
optimised for a different type of fall with a different initial
state).
There is therefore provided an improved technique for fall
detection that can make use of information obtained by sensors in
the environment of the subject to improve the reliability of fall
detection, and to improve the reliability of the detection of
different types of falls.
Variations to the disclosed embodiments can be understood and
effected by those skilled in the art in practicing the principles
and techniques described herein, from a study of the drawings, the
disclosure and the appended claims. In the claims, the word
"comprising" does not exclude other elements or steps, and the
indefinite article "a" or "an" does not exclude a plurality. A
single processor or other unit may fulfil the functions of several
items recited in the claims. The mere fact that certain measures
are recited in mutually different dependent claims does not
indicate that a combination of these measures cannot be used to
advantage. A computer program may be stored or distributed on a
suitable medium, such as an optical storage medium or a solid-state
medium supplied together with or as part of other hardware, but may
also be distributed in other forms, such as via the Internet or
other wired or wireless telecommunication systems. Any reference
signs in the claims should not be construed as limiting the
scope.
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